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Machine vision for robotic manufacturing systems
Vávra, Daniel ; Bradáč, František (referee) ; Vetiška, Jan (advisor)
This master thesis focuses on creation of program in TwinCAT Vision environment for contour vertices detection on specified object using a camera. Teoretical part focuses on state of the art in the field of machine vision, including hardware components and introduction to image processing. Practical part deals with the program creation, including real assembly of the testing workplace with the camera. This thesis also deals with the robot guidance for object handling, therefore an example of gripper concept for specified object handling is designed. At the end of the practical part the created program is tested. Testing results are part of the digital attachment of this thesis.
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Složení snímku prstu s viditelným krevním řečištěm.
Pekárek, Jakub ; Semerád, Lukáš (referee) ; Rydlo, Štěpán (advisor)
This thesis describes a method for creating a composite image with a visible bloodstream. The bloodstream is detected by the Maximum Curvature algorithm. After that it was experimented with detecting and matching keypoints via algorithms SIFT and ORB. However, the resulting matches from these algoritms were not good enough, therefore the keypoints have to be manually assigned. From these keypoints, an estimated tranformation matrix was created. This matrix was then used for connecting two images. A cycle of assigning keypoints and connecting images is repeated even for newly created images. The final composite image with a visible bloodstream is created after connecting last two images. This composite image can be further used for verification or identification people.
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Object Detection on the i.MX RT Microcontroller
Kravchuk, Marina ; Rozman, Jaroslav (referee) ; Janoušek, Vladimír (advisor)
This work focuses on the use of machine learning, particularly convolutional neural networks, in industrial applications. The course of work involves investigating the implementation of these networks directly on embedded devices, specifically NXP i.MX RT microcontrollers. During the course of the study, materials related to the training and use of neural networks and their optimization for deployment on low power devices were reviewed. Several neural network models were trained and tested, the best of which was used in the final version of the application. The application itself is divided into two parts: one part is written in C/C++ in the MCUXpresso IDE, where the main functionality of the program is implemented, while the other part of the work, i.e. the creation of a graphical user interface to control the program, is done in Python. The result is a functional application for the MIMXRT1170-EVK microcontroller that is able to detect and recognize small colored objects of certain shapes from a predefined data set.
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Optical and mechanical tracking of a moving object
Chaloupka, Radim ; Zemčík, Tomáš (referee) ; Honec, Peter (advisor)
The bachelor's thesis deals with the design of a pan-tilt platform with a camera, the design of controllers for its control, and the design and implementation of algorithms for the detection of predefined, moving objects. The task of the system is to find the object on the scene and maintain its position so that it is always in the center of the image sensor. The work uses the C++ programming language and Raspberry Pi hardware. PS and PSD controllers designed by the Ziegler-Nichols method are used for pan-tilt platform positioning, and interest point detection methods implemented in OpenCV libraries are used for optical tracing.
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Mobile Application for Scanning Nonograms and Solving It
Zobaník, Michal ; Pánek, Richard (referee) ; Dyk, Tomáš (advisor)
The goal of this bachelor thesis is to create mobile application for scanning nonograms from newspapers or magazines and allows its solving. The thesis describes the design of the application, its functionality and important parts of implementation. Image processing methods are used for detection of the nonogram. Number recognition is realized by created and trained convolutional network. Scanned nonograms are solved by using several logical rules and backtracking.
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Rozpoznávání nemocí rostlin pomocí umělé inteligence
Juliš, Adam ; Kubík, Tibor (referee) ; Bažout, David (advisor)
The aim of this work was to investigate the possibility of plant disease detection in the ab- sence of training data. The possibility of extracting the pattern of each disease and apply- ing these patterns to unknown plants was investigated. While still in the theoretical part of the thesis, this approach was found to be flawed. Furthermore, datasets with images of plant pathogens were analyzed and compared. An augmented image generator and several models were created over a smaller dataset validating existing approaches
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Automating the search for abnormalities from tomographic data
Semerák, Petr ; Zemek, Marek (referee) ; Bazala, Jiří (advisor)
Předložená diplomová práce se zabývá automatizací procesu skenování a vyhodnocování abnormalit gascoolerů, které vznikají při jejich výrobě. Nedestruktivní testování využívá technologii CT skenování jejímž výstupem jsou obrazová data. Cílem práce je nahradit zdlouhavý proces ručního procházení dat spolehlivou algoritmizovatelnou metodou a ověřit, zda je tento směr vývoje perspektivní. V teoretické části se práce zabývá chladícími systémy v automobilech, nedestruktivním testování s těžištěm v CT technologii a rešerší softwarů pro prohlížení a analýzu CT dat. Praktická část diplomové práce se zaměřuje na problematiku ucpaných kanálků gascoolerů. Jsou popsány příčiny vzniku této abnormality, dosavadní přístup k jejímu odhalení a nový automatický přístup kontroly. Navržený algoritmus spolu s aplikaci vytvořenou pomocí Matlabu jsou testovány na konkrétních datech. Spolehlivost výsledků je v závěru porovnávána ručním hodnocením CT snímků. Pro hodnocení kvality obrazových dat je trénována hluboká neuronová síť.
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Evaluation of Image Quality and Camera Setup
Ondris, Ladislav ; Fučík, Otto (referee) ; Zemčík, Pavel (advisor)
Tato práce si klade za cíl vyvinout základ pro univerzální fotoaparát schopný aktualizovat své parametry na základě pozorované scény. Tento přístup kombinuje metriky hodnocení kvality obrazu s rozpoznáváním scény. Byla shromážděna sada metrik, jako jsou ty používané k vyhodnocení kontrastu a ostrosti. Kromě toho byl vyvinut model strojového učení pro rozpoznávání scény, který má sloužit jako základ pro výběr vhodných parametrů fotoaparátu přizpůsobených konkrétní scéně. Práce demonstruje praktické využití metrik k optimalizaci vybraných parametrů obrazového procesoru a k detekci optických aberací.
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Model Train Controlled by Computer
Černák, Filip ; Maršík, Lukáš (referee) ; Zemčík, Pavel (advisor)
The aim of this work was to simplify the automatic control of a model railway using a mobile device. The selected problem is solved using a program that scans the given railway. It detects model trains in the images and compares whether they are located in the areas marked by the user on them. If this occurs, the program will send a sequence of train and area specific commands to the specified command station. The created solution makes it possible to control switches and signals automatically, set the speed of locomotives in individual sections of the track and stop trains at stations. Furthermore, the solution will not allow two model trains to enter one section of the track. The benefit of this work is a simple way of automatically controlling a model railway for beginning modelers. The work gradually describes the concept of the solution, its subsequent implementation and the results of tests verifying the fulfillment of the assignment.
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